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Originally published as Genetics Published Articles Ahead of Print on February 3, 2008.
Genetics, Vol. 178, 1795-1805, March 2008, Copyright © 2008
doi:10.1534/genetics.107.081216
Applying Gene Expression, Proteomics and Single-Nucleotide Polymorphism Analysis for Complex Trait Gene Identification
Ioannis M. Stylianou*,1,
Jason P. Affourtit*,2,
Keith R. Shockley*,
Robert Y. Wilpan*,
Fadi A. Abdi
,
Sanjeev Bhardwaj,
Jarod Rollins*,
Gary A Churchill* and
Beverly Paigen*,3
* The Jackson Laboratory, Bar Harbor, Maine 04609 and Applied Biosystems, Framingham, Massachusetts 01701
3 Corresponding author: The Jackson Laboratory, 600 Main St., Bar Harbor, ME 04609.
E-mail: bev.paigen{at}jax.org
Previous quantitative trait locus (QTL) analysis of an intercross involving the inbred mouse strains NZB/BlNJ and SM/J revealed QTL for a variety of complex traits. Many QTL have large intervals containing hundreds of genes, and methods are needed to rapidly sort through these genes for probable candidates. We chose nine QTL: the three most significant for high-density lipoprotein (HDL) cholesterol, gallstone formation, and obesity. We searched for candidate genes using three different approaches: mRNA microarray gene expression technology to assess >45,000 transcripts, publicly available SNPs to locate genes that are not identical by descent and that contain nonsynonymous coding differences, and a mass-spectrometry-based proteomics technology to interrogate nearly 1000 proteins for differential expression in the liver of the two parental inbred strains. This systematic approach reduced the number of candidate genes within each QTL from hundreds to a manageable list. Each of the three approaches selected candidates that the other two approaches missed. For example, candidate genes such as Apoa2 and Acads had differential protein levels although the mRNA levels were similar. We conclude that all three approaches are important and that focusing on a single approach such as mRNA expression may fail to identify a QTL gene.